In 2019, [1]} defined the scenarios of HFL, VFL, and FTL. The definitions use the symbols \(X\)  to mean features, \(Y\)  for labels, \(I\)  for the IDs of participants, and \(D\)  for the local datasets. Then, an HFL scenario is characterized as \(X_i = X_j, Y_i = Y_j, I_i \ne I_j, \forall D_i, D_j, i \ne j\) . A VFL scenario can be identified as \(X_i \ne X_j, Y_i \ne Y_j, I_i = I_j, \forall D_i, D_j, i \ne j\) . Lastly, an FTL scenario has \(X_i \ne X_j, Y_i \ne Y_j, I_i \ne I_j, \forall D_i, D_j, i \ne j\) . The authors also distinguished FL from distributed ML. Despite being very similar, in FL, users have autonomy and the central server cannot control their participation in the training process. FL also has an emphasis on privacy protection, while distributed ML does not.
